From Tasks to Topology: Dorsal and Ventral Streams Emerge in Optimized Neural Networks
Reza, T.; Jordan, E.; Luo, S.; Patel, K.; Tang, J.; Niemeier, M.
Show abstract
The primate visual system is organized into dorsal and ventral pathways, classically linked to visuomotor control and perception. A long-standing question is whether this division reflects intrinsic architectural priors or emerges from task demands. We trained a single convolutional network to perform classification and grasp prediction of 3D objects, without imposing modular structure. Dual-stream topology - functionally distinct visuomotor and perceptual pathways - emerged spontaneously with rich cross-communication. Shapley value analyses revealed that action- and perception-selective features developed progressively across depth, reflecting task-driven hierarchical specialization. Time-resolved EEG showed that model activity mapped onto dissociable temporal components in human cortex: ventral-aligned signals emerged early and late, where dorsal- and ventral-aligned responses coincided in the intervening interval. These results demonstrate that task optimization alone can explain core features of dorsal-ventral organization, and that distinct temporal roles for perception and action arise naturally atop a shared feedforward scaffold, without requiring architectural hard-coding or recurrence.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Inferring functional organization of posterior parietal cortex circuitry based on information flow 96%
- Functional harmonics reveal multi-dimensional basis functions underlying cortical organization 96%
- Stimulus information guides the emergence of behavior related signals in primary somatosensory cortex during learning 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.